In the era of digitalization, manufacturing companies expect their growing access to data to lead to improvements and innovations. Manufacturing engineers will have to collaborate with data scientists to analyze the ever-increasing volume of data. This process of adopting data science techniques into an engineering organization is a sociotechnical process fraught with challenges. This article uses a participant observation case study to investigate and discuss the sociotechnical nature of the adoption data science technology into an engineering organization. In the case study, a young data scientist/statistician interacted with experienced production engineers in a global automotive organization to mutual satisfaction. However, the case study highlights the mis-aligned expectations between engineers and data scientists and knowledge in what is necessary to successfully benefit from manufacturing process data. The results reveal that the engineers had an initially romantic and idealistic view on how data scientists can bring value out of dispersed and complex information residing in the multisite manufacturing organization’s datasets in a “magic” way. Conversely, the data scientist had not enough engineering and contextual understanding to ask the right questions. The case reveals important shortcomings in the sociotechnical processes that undergo changes as digitalization is brought into mature engineering organizations and points to a lack of knowledge on multiple levels of the data analysis process and the ethical implications this could have.
The conditions under which the data wrangling process is undertaken has a profound impact on the quality of the results of the data wrangling and analysis. This paper presents the results of the analysis of the socio-technical aspects of a data wrangling activity in a large, multi-site global manufacturer. This activity was technically demanding, as operational data from multiple sources and formats needed to be integrated, but also involved interaction with multiple stakeholders in different parts of the world with their own ways of collecting and structuring the data. The data had been captured previously for a different purpose. The clients were not aware that the data followed a different logic in the various sites and in some cases needed to be manually extracted and interpreted. The paper describes the data wrangling process and analyses the assumptions, goals and biases of the different stakeholders. The analysis raises questions and insights about how data can be trusted, and suggests that human intervention with data along the data wrangling process is often un-intentional, tacit and easily overlooked. It is suggested that contextual factors, such as data quality and assessment of consequences when acting/making decisions on the new data set is given higher attention during the specification of data wrangling assignments. The paper concludes with recommendations for data wrangling practitioners.
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